Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

2019 ◽  
Vol 29 (9) ◽  
pp. 4765-4775 ◽  
Author(s):  
Burak Kocak ◽  
Ece Ates ◽  
Emine Sebnem Durmaz ◽  
Melis Baykara Ulusan ◽  
Ozgur Kilickesmez
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Clear Cell ◽  
High Dimensional ◽  
Quantitative Ct ◽  
Reproducibility Study ◽  
Ct Texture Analysis

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Radiology ◽  
2015 ◽  
Vol 276 (3) ◽  
pp. 787-796 ◽  
Author(s):  
Taryn Hodgdon ◽  
Matthew D. F. McInnes ◽  
Nicola Schieda ◽  
Trevor A. Flood ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Ct Images ◽  
Renal Angiomyolipoma ◽  
Quantitative Ct ◽  
Unenhanced Ct ◽  
Ct Texture Analysis

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

2021 ◽  
pp. 028418512110449
Author(s):  
Yoshiharu Ohno ◽  
Kota Aoyagi ◽  
Daisuke Takenaka ◽  
Takeshi Yoshikawa ◽  
Yasuko Fujisawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Disease Severity ◽  
Thin Section ◽  
Ct Scans ◽  
Normal Lung ◽  
Quantitative Index ◽  
Serial Ct ◽  
Ct Texture Analysis

Background The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses ( P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

scholarly journals Quantitative CT texture analysis for diagnosing systemic sclerosis

Medicine ◽  
2019 ◽  
Vol 98 (29) ◽  
pp. e16423 ◽  
Author(s):  
Gianluca Milanese ◽  
Manoj Mannil ◽  
Katharina Martini ◽  
Britta Maurer ◽  
Hatem Alkadhi ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Texture Analysis ◽  
Quantitative Ct ◽  
Ct Texture Analysis

Distinguishing fibrosis/necrosis from teratoma or viable disease in the retroperitoneum in post-chemotherapy, nonseminomatous testicular germ cell tumor using quantitative CT texture analysis.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 563-563
Author(s):  
Kevin George King ◽  
Sumeet Bhanvadia ◽  
Saum Ghodoussipour ◽  
Darryl Hwang ◽  
Bino Varghese ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Germ Cell ◽  
Texture Analysis ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Gray Level ◽  
Square Root ◽  
Quantitative Ct ◽  
Testicular Germ Cell ◽  
Ct Texture Analysis

563 Background: In metastatic nonseminomatous testicular germ cell tumor (NSGCT), post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) is indicated for residual masses > 1 cm because of these 45% will be fibrosis/necrosis, 45% will be teratoma and 15% will be viable malignancy. There is no imaging test that reliably distinguishes lymph nodes (LNs) with tumor (teratoma or malignancy) from LNs with fibrosis/necrosis. We evaluated whether quantitative CT texture analysis (TA) could make this differentiation. Methods: Pre- and post-chemotherapy CTs (all same phase and slice thickness) were reviewed in 22 NSGCT patients with RP LNs > 1 cm post chemotherapy. After manual segmentation of RP LNs on a 3D workstation, 187 TA metrics were derived, using 2D/3D gray-level co-occurrence matrix (GLCM), 2D/3D gray-level difference matrix (GLDM), and spectral analysis. Metrics were derived 2 ways: from post-chemotherapy CTs alone, and also as a difference between pre- and post-chemotherapy CTs, resulting in 374 metrics. PC-RPLND pathology was correlated with CT data at 88 LN stations in these 22 patients. Results: 15 imaging metrics showed a significant difference (p ≤ 0.05) between LN stations with only fibrosis/necrosis and those with teratoma or viable tumor. Seven were derived from the difference between pre- and post-chemotherapy CTs: 4 using a 2D GLCM (coronal standard deviation, coronal square root of variance, coronal mean, and coronal sum of average), and 3 using a 2D GLDM (axial variance, axial square root of variance, and coronal variance). The other 8 were derived from post-chemotherapy CTs alone: 7 using a 2D GLCM (sagittal square root of variance, sagittal standard deviation, coronal square root of variance, coronal mean, coronal standard deviation, coronal sum of average, and coronal entropy) and 1 using a 2D GLDM (sagittal sum entropy). Conclusions: CT TA shows promise in differentiating necrosis from teratoma or viable tumor in RP LNs in post-chemotherapy NSGCT. A larger study is needed to further test this method, towards a long-term goal of potentially allowing some patients to avoid PC-RPLND.

scholarly journals Development of CT texture analysis in COPD and association with visual scoring and DLCO

2021 ◽  
Author(s):  
V. Lui ◽  
W. C. Tan ◽  
J. C. Hogg ◽  
H. O. Coxson ◽  
M. Kirby
Keyword(s):  
Lung Function ◽  
Healthy Volunteers ◽  
Texture Analysis ◽  
Texture Features ◽  
Visual Assessment ◽  
Quantitative Ct ◽  
Visual Scoring ◽  
Ct Texture Analysis

Objectives: • To determine if CT texture features, such as GLCM and FD, can differentiate patients with COPD from healthy volunteers, and are related to lung function • To determine if CT texture features are association with qualitative visual scoring • To determine if CT texture features are significantly associated with COPD outcomes, independent of qualitative scoring and standard quantitative CT emphysema measurements Hypothesis: • CT texture features can be developed to objectively aid in quantifying the severity of emphysema, and may provide information complementary to qualitative visual assessment

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